This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).
提供：
Python 応用データサイエンス の専門講座ミシガン大学（University of Michigan）このコースについて
293,149 最近の表示
受講生の就業成果
32%
コース終了後に新しいキャリアをスタートした
34%
コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の4コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約26時間で修了
英語
字幕：英語, 韓国語
学習内容
Understand how text is handled in Python
Apply basic natural language processing methods
Write code that groups documents by topic
Describe the nltk framework for manipulating text
習得するスキル
Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing
受講生の就業成果
32%
コース終了後に新しいキャリアをスタートした
34%
コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の4コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約26時間で修了
英語
字幕：英語, 韓国語
提供：
ミシガン大学（University of Michigan）
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
シラバス - 本コースの学習内容
8時間で修了
Module 1: Working with Text in Python
8時間で修了
5件のビデオ （合計56分）, 4 readings, 3 quizzes
5件のビデオ
Handling Text in Python18 分
Regular Expressions16 分
Demonstration: Regex with Pandas and Named Groups5 分
Internationalization and Issues with Non-ASCII Characters12 分
4件の学習用教材
Course Syllabus10 分
Help us learn more about you!10 分
Notice for Auditing Learners: Assignment Submission10 分
Resources: Common issues with free text10 分
2の練習問題
Practice Quiz8 分
Module 1 Quiz12 分
6時間で修了
Module 2: Basic Natural Language Processing
6時間で修了
3件のビデオ （合計36分）
3件のビデオ
Basic NLP tasks with NLTK16 分
Advanced NLP tasks with NLTK16 分
2の練習問題
Practice Quiz4 分
Module 2 Quiz10 分
7時間で修了
Module 3: Classification of Text
7時間で修了
7件のビデオ （合計94分）
7件のビデオ
Identifying Features from Text8 分
Naive Bayes Classifiers19 分
Naive Bayes Variations4 分
Support Vector Machines24 分
Learning Text Classifiers in Python15 分
Demonstration: Case Study - Sentiment Analysis9 分
1の練習問題
Module 3 Quiz14 分
6時間で修了
Module 4: Topic Modeling
6時間で修了
4件のビデオ （合計58分）, 2 readings, 3 quizzes
4件のビデオ
Topic Modeling8 分
Generative Models and LDA13 分
Information Extraction18 分
2件の学習用教材
Additional Resources & Readings10 分
Post-Course Survey10 分
2の練習問題
Practice Quiz4 分
Module 4 Quiz10 分
レビュー
APPLIED TEXT MINING IN PYTHON からの人気レビュー
RKにより2019年7月19日
Course is great except for the auto grader issues. Please look into the issue. I would like to take this opportunity and thank Prof V. G. Vinod Vydiswaran and all those who helped me to complete it.
CCにより2017年8月26日
Quite challenging but also quite a sense of accomplishment when you finish the course. I learned a lot and think this was the course I preferred of the entire specialization. I highly recommend it!
JSにより2017年8月22日
Very good course with very good material and teachers. I just missed some more practical examples to follow along the classes, and more further readings (specially for information extraction).
LCにより2018年2月16日
Love the focus on conceptual text processing and practical guides to implementation in python, but the assignment grader was extremely specific for no reason, especially the Week3 assignment.
Python 応用データサイエンス専門講座について
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
よくある質問
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
©2020 Coursera Inc.All rights reserved.
